High-strength low-alloy (HSLA) steels often comprise of Cu clusters and M2C (M: Mo, Cr) carbides as strengthening particles. In this work, three new HSLA steels with alternative strengthening phases, Fe2SiTi and Ni3Ti, are investigated by using HSLA-115 steel as the reference. To evaluate the weldability for potential fabrication using casting, welding, and additive manufacturing, freezing ranges are studied using differential thermal analysis (DTA), and CALPHAD (Calculation of Phase Diagrams) approach under equilibrium and nonequilibrium conditions. While the cooling signals in the DTA analysis are not pronounced enough for thermal analysis, the trend of freezing range change based on the nonequilibrium and equilibrium calculations are consistent with the heating signals. High-throughput calculations are performed to deduce the effect of variation of each alloying element on the freezing range. Moreover, the experimental and calculated phase fractions and compositions of the as-cast and heat-treated alloys were compared. Though CALPHAD model-prediction can provide valuable insights into the phase stability of these new alloys, there is a remarkable difference regarding phase fraction and composition of individual phases. Therefore, this study indicates that the application of the CALPHAD approach in new alloy discovery requires a careful model-validation and database calibration.

中文翻译：

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用于焊接和增材制造的具有 Heusler 相强化的新型高强度低合金钢的热力学研究：高通量 CALPHAD 计算和数据库验证的关键实验

高强度低合金 (HSLA) 钢通常包含 Cu 簇和 M2C（M：Mo，Cr）碳化物作为强化颗粒。在这项工作中，以 HSLA-115 钢为参考，研究了三种具有交替强化相的新型 HSLA 钢 Fe2SiTi 和 Ni3Ti。为了评估使用铸造、焊接和增材制造的潜在制造的可焊性，在平衡和非平衡条件下使用差热分析 (DTA) 和 CALPHAD（相图计算）方法研究冻结范围。虽然 DTA 分析中的冷却信号不足以进行热分析，但基于非平衡和平衡计算的冻结范围变化趋势与加热信号一致。执行高通量计算以推断每种合金元素的变化对冻结范围的影响。此外，还比较了铸态和热处理合金的实验和计算相分数和成分。尽管 CALPHAD 模型预测可以为这些新合金的相稳定性提供有价值的见解，但在相分数和各个相的组成方面存在显着差异。因此，本研究表明 CALPHAD 方法在新合金发现中的应用需要仔细的模型验证和数据库校准。尽管 CALPHAD 模型预测可以为这些新合金的相稳定性提供有价值的见解，但在相分数和各个相的组成方面存在显着差异。因此，本研究表明 CALPHAD 方法在新合金发现中的应用需要仔细的模型验证和数据库校准。尽管 CALPHAD 模型预测可以为这些新合金的相稳定性提供有价值的见解，但在相分数和各个相的组成方面存在显着差异。因此，本研究表明 CALPHAD 方法在新合金发现中的应用需要仔细的模型验证和数据库校准。